Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the geometric information collected by LiDAR sensors has been widely combined with optical images to provide supplementary spectral information to achieve more precise results in diverse remote sensing applications. The emergence of active Multispectral LiDAR (MSL) systems, which operate on different wavelengths, has recently been revolutionizing the simultaneous acquisition of height and intensity information. So far, MSL technology has been successfully applied for fine-scale mapping in various domains. However, a comprehensive review of this modern technology is currently lacking. Hence, this study presents an exhaustive overview of the current state-of-the-art in MSL systems by reviewing the latest technologies for MSL data acquisition. Moreover, the paper reports an in-depth analysis of the diverse applications of MSL, spanning across fields of “ecology and forestry”, “objects and Land Use Land Cover (LULC) classification”, “change detection”, “bathymetry”, “topographic mapping”, “archaeology and geology”, and “navigation”. Our systematic review uncovers the potentials, opportunities, and challenges of the recently emerged MSL systems, which integrate spatial–spectral data and unlock the capability for precise multi-dimensional (nD) mapping using only a single-data source.

Regular forest inventory is essential for conservation and management. Over the past decades, laser scanning has emerged as a remote and non-destructive solution to streamline this laborious process. Advanced multispectral (MS) laser scanning systems simultaneously acquire 3D spatial and spectral information across multiple wavelengths, enabling estimation of forest biophysical and biochemical traits. This study investigates the potential of airborne MS laser scanning for fine-grained forest semantic segmentation into six components (ground, low vegetation, trunk, branches, foliage, and woody debris), thereby supporting forest inventory and analysis. We evaluate three state-of-the-art 3D deep learning models (kernel point convolution (KPConv), superpoint transformer (SPT), and point transformer V3 (PTv3)) and random forest model. Our analysis reveals the superiority of PTv3, outperforming the other models by 21.8 percentage points (pp) with the mean intersection over union (mIoU) of 69.1%. Additionally, our rigorous spectral ablation study demonstrates that MS laser scanning data substantially improves the segmentation results, increasing the IoU of woody debris, branches, and trunks by 12.7 pp, 4.5 pp, and 2.5 pp, respectively. This study highlights the strong potential of MS laser scanning to enable automated and accurate forest inventory through prior fine-grained forest semantic segmentation.

Accurate estimation of forest structural and taxonomic parameters is vital for biodiversity monitoring, carbon accounting and sustainable management. Most of the current methods for estimating these parameters are still developed and tested on site-specific case studies, limiting reproducibility and cross-site generalization. This paper introduces 3D3, a multi-sensor and multi-resolution benchmark dataset designed to evaluate 3D forestry algorithms across diverse European forest types. 3D3 includes data collected by airborne, helicopter, UAV and terrestrial (static and mobile) laser scanning systems along with RGB and hyperspectral imagery, covering a variety of forest types (Boreal, Alpine and Mediterranean). By encompassing both mono- and multi-wavelength laser data, 3D3 represents a unique resource for developing new algorithms and evaluating them on distinct datasets. Each site provides ground truth for at least one task among Individual Tree Segmentation (ITS), Forest Semantic Segmentation (FSS) or tree parameter estimation and species classification.